class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Lemmatize function for all words in WordNet.
  
  :param pos: POS tag pr a list of POS tag.
  :return: A list of lemmas that have the given pos tag.
docstr_tok: ["Lemmatize","function","for","all","words","in","WordNet",".",":","param","pos",":","POS","tag","pr","a","list","of","POS","tag",".",":","return",":","A","list","of","lemmas","that","have","the","given","pos","tag","."]
code: def get_all_lemmas(self, pos):
      r"""
      Lemmatize function for all words in WordNet.
  
      :param pos: POS tag pr a list of POS tag.
      :return: A list of lemmas that have the given pos tag.
  
      """
      self.check_initialized()
  
      if self.__wordnet is None:
          self.__wordnet = self.model_manager.load(NLTK_WORDNET)
  
      if not isinstance(pos, list):
          return self.__wordnet.all_lemma(pos)
      else:
          return [self.__wordnet.all_lemma(_pos) for _pos in pos]
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 15 for calling the EnProcessor obj's underlying member method code with example usage: obj.get_all_lemmas(pos) and returns: self, unknown